System and method for characterizing brain states during general anesthesia and sedation using phase-amplitude modulation

ABSTRACT

A system and method for monitoring and/or controlling a state of consciousness of a subject experiencing anesthesia are provided. In some aspects, the system includes a plurality of sensors placed about the subject and configured to acquire electroencephalogram (“EEG”) data therefrom while the subject is receiving anesthesia, and at least one processor configured to receive the EEG data from the plurality of sensors, and perform a phase-amplitude coupling analysis using the received EEG data to determine a phase-amplitude frequency distribution. The at least one processor is also configured to identify a state of consciousness of the subject using the determined phase-amplitude frequency distribution, and generate a report indicative of the state of consciousness of the subject.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is based on, claims priority to, and incorporatesherein by reference in its entirety U.S. Provisional Application Ser.No. 61/927,104, filed Jan. 14, 2014, and entitled, “SYSTEM AND METHODCHARACTERIZING BRAIN STATES DURING GENERAL ANESTHESIA AND SEDATION USINGPHASE-AMPLITUDE MODULATION.”

BACKGROUND OF THE INVENTION

The present disclosure generally relates to systems and method formonitoring a state of a subject and, more particularly, to systems andmethods for appropriate monitoring and controlling states of a subjectreceiving a dose of anesthetic compound(s) or, more colloquially,receiving a dose of “anesthesia” or sedation.

Although the molecular actions of many anesthetic drugs at specificreceptors are known, alterations in network dynamics that disruptinformation processing and produce unconsciousness have remainedelusive. Typically, loss of consciousness is accompanied by increasedelectroencephalogram (“EEG”) power across a broad range of frequenciesless than 40 Hz. Traditional analyses, including visual interpretationof EEG traces and time-frequency power spectral analysis, arecomputationally simple and play a central role in neurophysiology andclinical EEG applications. However, power spectral analysis treats theEEG as a collection of independent frequency bands, offering limitedinsight into the modulation of network activity as a whole. Becausecortical networks frequently express oscillations in multiple frequencybands simultaneously, nonlinear biophysical processes, such as neuronalspiking, induce cross-frequency coupling, which is undetectable byspectral analysis. Identifying global brain states, such as sleep stagesor general anesthesia-induced unconsciousness, remains a significantchallenge for understanding cortical dynamics. Moreover, an EEG-basedframework for understanding brain state transitions during generalanesthesia will be critical for improving subject monitoring to avoidcomplications, such as intra-operative awareness.

Given the above, there remains a need for systems and methods thataccurately characterize brain states of subjects subjected to anesthesiaor sedation.

SUMMARY OF THE INVENTION

The present disclosure provides systems and methods directed tomonitoring and controlling subjects using acquired physiological data,for use in certain medical procedures, such as general anesthesia andsedation. Specifically, the present invention provides systems andmethods.

In accordance with one aspect of the disclosure, a system for monitoringand/or controlling a state of consciousness of a subject experiencinganesthesia are provided. The system includes a plurality of sensorsplaced about the subject and configured to acquire electroencephalogram(“EEG”) data therefrom while the subject is receiving anesthesia, and atleast one processor configured to receive the EEG data from theplurality of sensors, and perform a phase-amplitude coupling analysisusing the received EEG data to determine a phase-amplitude frequencydistribution. The at least one processor is also configured to identifya state of consciousness of the subject using the determinedphase-amplitude frequency distribution, and generate a report indicativeof the state of consciousness of the subject.

The foregoing and other advantages of the invention will appear from thefollowing description. In the description, reference is made to theaccompanying drawings which form a part hereof, and in which there isshown by way of illustration a preferred embodiment of the invention.Such embodiment does not necessarily represent the full scope of theinvention, however, and reference is made therefore to the claims andherein for interpreting the scope of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will hereafter be described with reference to theaccompanying drawings, wherein like reference numerals denote likeelements.

FIG. 1A is schematic block diagram of an example physiologicalmonitoring system, in accordance with aspects of the present disclosure.

FIG. 1B is schematic block diagram of another example physiologicalmonitoring systems, in accordance with aspects of the presentdisclosure.

FIG. 2 is an illustration of an example system for use in accordancewith the present disclosure.

FIG. 3 is a flowchart illustrating the steps in a process, in accordancewith the present disclosure.

FIG. 4 is an example physiological monitoring system, in accordance withaspects of the present disclosure.

FIG. 5 is a graphical illustration showing a phase-coupling transitionduring general anesthesia using profopol.

FIG. 6A are graphical illustrations showing phase-amplitude couplingsfor a number of subjects during anesthesia.

FIG. 6B is a graphical illustration showing a mean coupling patternacross a population of subjects at various drug concentrations.

FIG. 6C is a graphical illustration showing mean concentration ofpropofol at loss of consciousness, recovery of consciousness, and duringtrough-max and peak-max epochs.

FIG. 6D is a graphical illustration showing median modulation index andmodulation phase across a population of subjects as a function of thecenter of the frequency based used for phase and amplitude.

FIG. 7 is a graphical illustration showing that peak-max coupling is notidentical with burst suppression brain state.

FIG. 8 is a graphical illustration showing spatial distribution ofphase-amplitude coupling at baseline, trough-max and peak-max epochs.

FIG. 9 is a graphical illustration showing average modulation index atcortical patches estimated by source localization analysis of EEGsignals.

FIG. 10 is a graphical illustration of locations of intracranialcortical surface electrodes mapped to an average cortical surface.

DETAILED DESCRIPTION

Rhythmic oscillations shape cortical dynamics during active behavior,sleep, and general anesthesia. Among other signatures, cross-frequencyphase-amplitude coupling is a prominent feature of corticaloscillations, but its role in organizing conscious and unconscious brainstates is poorly understood. Using high-density EEG and intracranialelectrocorticography during gradual induction of propofol generalanesthesia in humans, the present disclosure describes results fromrapid drug-induced transitions between distinct states with distinctphase-amplitude coupling and cortical source distributions. Thesedistinct states of phase-amplitude coupling reflect different states ofarousal or consciousness. These results, provide objectiveelectrophysiological landmarks of distinct unconscious brain states, andcould be used to help improve EEG-based monitoring during generalanesthesia and sedation, as will be described.

In particular, the present disclosure recognizes that informationnecessary for accurately identifying and characterizing distinct globalbrain states may be contained in patterns of coupling between distinctfrequency bands. Thus, systems and methods are provided herein thatimplement such information, and other information, for purposes ofmonitoring and controlling a subject during administration of anesthesiaor sedation.

In particular, one form of cross-frequency interaction isphase-amplitude coupling, in which power within one frequency band waxesand wanes at specific phases of an underlying, lower-frequency rhythm.Phase-amplitude coupling is widespread during sleep, waking, and generalanesthesia, and has been related to attention and behavior in human andprimate cortex. In previous work by the inventors using scalp EEGrecordings during propofol-induced general anesthesia, two forms ofcoupling were discovered between the phase of low-frequency activity(“LFA”), which is typically in a frequency range roughly between 0.1 to2 Hz, and the amplitude of rhythms in a frequency range roughly between8 and 14 Hz. In particular, one form of coupling can occur during thetransitions to and from an unconscious state, herein referred to as“trough-max”, whereas another can occur at deep levels ofunconsciousness, herein referred to as “peak-max.” The cortical networksinvolved in these distinct modulation patterns, their frequencydependence, and their relationship to other brain states, such as burstsuppression, are presently unclear.

Therefore, results from investigations of cortical generators of suchmodulation patterns are presented herein using source localizationanalyses and intracranial electrocorticography. In addition, analysis oftheir frequency dependence is performed, demonstrating effects onindividual subjects, and clarifying their relationship to burstsuppression. In particular, findings show that patterns ofcross-frequency coupling reflect dynamics within distinct corticalnetworks and identify transitions in global brain states.

Referring now to the drawings, FIGS. 1A and 1B illustrate examplesubject monitoring systems and sensors that can be used to providemonitoring of a subject, for instance, during administration anesthesia,sedation, or other medical procedure.

For example, FIG. 1A shows an embodiment of a monitoring system 10.Specifically, using monitoring system 10, a subject 12 is monitoredusing a sensor assembly 13 included therein, which can transmit varioussignals over a cable 15 or other communication link or medium to aphysiological monitor 17. The physiological monitor 17 includes aprocessor 19 and, optionally, a display 11 for reporting a variety ofinformation, such as information related to the condition of the subject12, state of consciousness or transitions from states of consciousnessof the subject 12, as well as other states of the subject 12. In somedesigns, the monitoring system 10 may be a portable monitoring system.In other designs, the monitoring system 10 may be a pod without adisplay, and adapted to provide physiological parameter data to adisplay.

The sensor assembly 13 can include one or more sensing elements such as,for example, electrical EEG sensors, or the like. The sensor assembly 13can generate respective signals by measuring physiological parameters ofthe subject 12. The signals are then processed by one or more processors19. The one or more processors 19 then communicate the processed signalto the display 11, if provided, or other logically connected output. Inan embodiment, the display 11 is incorporated in the physiologicalmonitor 17. In another embodiment, the display 11 is separate from thephysiological monitor 17.

For clarity, a single block is used to illustrate the sensor assembly 13shown in FIG. 1A. However, it should be understood that the sensorassembly 13 shown is intended to represent one or more sensorsconfigured for placement at a variety of locations about the skull ofthe subject 12 and acquire various physiological signals. In oneembodiment, the sensor assembly 13 can include sensors of one type, suchas EEG sensor. In other embodiments, the sensor assembly 13 can includemultiple types of sensors, such as EEG sensors, brain oxygenationsensors, optical sensors, galvanic skin response sensors, and so on. Ineach of the foregoing embodiments, additional sensors of different typesare also optionally included. Other combinations of numbers and types ofsensors are also suitable for use with the monitoring system 10.

In some embodiments of the system shown in FIG. 1A, all of the hardwareused to receive and process signals from the sensors are housed withinthe same housing. In other embodiments, some of the hardware used toreceive and process signals is housed within a separate housing. Inaddition, the physiological monitor 17 of certain embodiments includeshardware, software, or both hardware and software, whether in onehousing or multiple housings, used to receive and process the signalstransmitted by the sensor assembly 13.

As shown in FIG. 1B, the sensor assembly 13 can include a cable 25. Thecable 25 can include three conductors, for example, within an electricalshielding. One conductor 26 can provide power to a physiological monitor17, one conductor 28 can provide a ground signal to the physiologicalmonitor 17, and one conductor 28 can transmit signals from one sensor inthe sensor assembly 13 to the physiological monitor 17. For multiplesensors, one or more additional cables 25 can be provided.

In some embodiments, the ground signal is an earth ground, but in otherembodiments, the ground signal is a subject ground, sometimes referredto as a subject reference, a subject reference signal, a return, or asubject return. In some embodiments, the cable 25 carries two conductorswithin an electrical shielding layer, and the shielding layer acts asthe ground conductor. Electrical interfaces 23 in the cable 25 canenable the cable to electrically connect to electrical interfaces 21 ina connector 20 of the physiological monitor 17. In another embodiment,the sensor 13 and the physiological monitor 17 communicate wirelessly.

Processor 19 may be configured to perform a number of steps forprocessing and analyzing data received from the sensor assembly 13. Inparticular, process 19 can be configured to assemble the data in anynumber of forms, including waveforms, spectrograms, coherograms,modulograms, and so on. Also, processor 19 can perform a phase-amplitudecoupling analysis to determine a phase-amplitude frequency distribution,and/or a phase-amplitude coupling spatial distribution, in accordancewith the present disclosure. Additionally, processor 19 may also beconfigured to identify other signal markers or signatures associatedwith the received the data using various analysis methods, includingwaveform analyses, spectral analyses, frequency analyses, coherenceanalyses and so on. For example, signal markers or signatures caninclude various signal amplitudes, phases, frequencies, power spectra,frequency distributions, spatial distribution, and so forth.

In some configurations, systems shown in FIGS. 1A and 1B may furtherinclude a memory, database or other data storage locations (not shown),accessible by processor 19, to include reference information or otherdata. Specifically, such reference information can include referencelistings or look-up tables including signals, signal markers orsignatures associated with specific anesthetic drugs, treatmentsconditions, and the like. In some aspects, such reference informationcan be used by the processor 19, optionally including user input orselections, to identify states of consciousness of a subject. Inparticular, processor 19 may process and analyze acquired data todetermine signal markers, signatures or patterns, includingphase-amplitude coupling frequency patterns, spatial distributionpatterns, phase-amplitude modulation polarity patterns, and so on.Subsequently, subject states of consciousness may be identified byperforming a comparison of the determined signal markers, signatures orpatterns with those categorized in the reference.

In other aspects, processor 19 may identify states of consciousness of asubject by acquiring and/or processing baseline data, such as baselinephase-amplitude frequency distribution data, a baseline polarity of thephase-amplitude modulation, and so on. States of consciousness may thenbe identified by processor 19 by performing a comparison of the baselinedata to the data received from the sensor assembly 13, for instance,while undergoing a medical procedure, such as receiving anesthesia orsedation.

Referring to FIG. 2, an example system 200 for use in carrying out stepsassociated with identifying or characterizing states of a subject, inaccordance with the present disclosure, is illustrated. The system 200includes an input 202, a pre-processor 204, a phase-amplitude analysisengine 206, a state analyzer 208, and an output 210. Some or all of themodules of the system 200 can be implemented by a patient monitor asdescribed above with respect to FIG. 1.

The pre-processor 204 may be designed to carry out any number ofprocessing steps for operation of the system 200. In addition, thepre-processor 204 may be configured to receive and pre-process datareceived from the input 202. In some aspects, pre-processor 204 may beconfigured to assemble the received data into in any number of forms,including time series waveforms. In addition, the pre-processor 204 maybe configured to perform any desirable noise rejection to filter anyinterfering signals associated with the data, as well as well as selectcomponents, for example, frequency components, associated with the data.

In some aspects, the pre-processor 204 may also be configured to receivean indication via the input 202, such as information related toadministration of an anesthesia compound or compounds, and/or anindication related to a particular subject profile, such as a subject'sage, height, weight, gender, or the like, as well as drug administrationinformation, such as timing, dose, rate, and the like.

In addition to the pre-processor 204, the system 200 may further includea phase-amplitude analysis engine 206, in communication with thepre-processor 202, designed to receive pre-processed data from thepre-processor 202 and carry out steps necessary for a phase-amplitudeanalysis, in accordance with aspects of the present disclosure, as wellas other analyses. In some aspects, may be configured to assemblemodulograms, spectrograms, coherograms, and so on, using data receivedfrom the pre-processor 206.

As a result, the phase-amplitude analysis engine 206 may provide datarelated to phase-amplitude frequency distributions, polarity of aphase-coupling modulation, spatial distribution of phase-amplitudemodulation, and so on, which may then be used by the brain stateanalyzer 208 to determine brain state(s) of the subject. For example,the brain state analyzer 208 may identify states of consciousness orsedation during administration of a drug with anesthetic properties,such as a loss of consciousness, a recovery from consciousness, or alevel of consciousness, as well as confidence indications related to thedetermined state(s). In some aspects, the brain state analyzer 208 mayutilize reference or baseline data, as described, in determining brainstate(s) of the subject.

Information related to the determined state(s) may then be relayed tothe output 210, along with any other desired information, in any shapeor form, intermittently or in real-time. In some aspects, the output 210may include a display configured to provide information or indicatorswith respect to denoised spectral decompositions, that may be formulatedusing spectrogram representations, either intermittently or in realtime.

Turning now to FIG. 3, a process 300 in accordance with aspects of thepresent disclosure is shown. Beginning with process block 302, anyamount of physiological data may be obtained, including EEG dataacquired from various locations about a subject's skull using, forexample, using systems described with respect to FIGS. 1A and 1B. Insome aspects, the physiological data may include baseline data.

Then at process block 304, a phase-amplitude coupling analysis, alongwith other analyses, may be performed using the received data. Inparticular, a number of phase-amplitude modulograms M(t, φ) may beconstructed, as detailed below, which characterize the relativeamplitude of activity in a frequency band f_(amp), as a function of thephase of the rhythm in band f_(ph). This can include processing receivedEEG signals, x(t), by applying a band-pass filter to extract eachfrequency band of interest, x_(b)(t), where b refers to the amplitudefrequency band or phase frequency band. Preferably, filters may chosento adequately isolate these frequency bands while allowing temporalresolution of changes in phase-amplitude coupling, which occur withinabout 2 min or less. A symmetric finite impulse response filters may beused using a least-squares approach. For example, using a MATLABfunction firls; f_(ph) passband=0.1-1 Hz, transition bands=0.085-0.1 and1-1.15 Hz, attenuation in stop band dB, filter order 2207; f_(amp),passband=8-13.9 Hz, transition bands=5.9-8.0 and 13.9-16.0 Hz,attenuation in stop band ≥60 dB, filter order 513. However, it may beappreciated that other frequencies filters, and other processingparameters may be utilized. A Hilbert transform may then be used toextract the instantaneous amplitude and phase.

Specifically a modulogram may be computed by assigning a given temporalsample to one of, say, N=18 equally spaced phase bins based on theinstantaneous value of ψ_(ph)(t), although other values are possible,and then averaging the corresponding values of A_(amp)(t) within say a 2min epoch as follows:

$\begin{matrix}{{{M\left( {t,\varphi} \right)} = \frac{\int_{t - \frac{\delta \; t}{2}}^{t + \frac{\delta \; t}{2}}{\int_{\varphi - \frac{\delta \; t}{2}}^{\varphi + \frac{\delta \; t}{2}}{{A_{amp}\left( t^{\prime} \right)}{\delta \left( {{\psi_{ph}\left( t^{\prime} \right)} - \varphi^{\prime}} \right)}d\; \varphi^{\prime}{dt}^{\prime}}}}{\int_{t - \frac{\delta \; t}{2}}^{t + \frac{\delta \; t}{2}}{{A_{amp}\left( t^{\prime} \right)}{dt}^{\prime}}}};} & (1)\end{matrix}$

where δ(t)=120 s and δφ=2 π/N. Note that

${{\sum\limits_{n = 1}^{N}{M\left( {t,\varphi_{n}} \right)}} = 1},{\varphi_{n} = {2\pi \; {n/N}}},$

so that M(t, φ) is normalized over phase bins. To reduce noise inestimated modulagrams, the value of M(t, φ) may be averaged frommultiple electrode locations. For example, six frontal electrodes Fz inthe standard montages may be averaged.

A modulation index, MI(t) may be defined as follows:

$\begin{matrix}{{{MI}(t)} = {\sum\limits_{n = 1}^{N}{{M\left( {t,\varphi_{n}} \right)}\log_{2}{\frac{M\left( {t,\varphi_{n}} \right)}{1/N}.}}}} & (2)\end{matrix}$

A statistical significance may be assessed by performing a permutationtest. For instance, a number of random time shifts, Δt, may be sampledfrom a uniform distribution on an interval, for example, between −60 secand +60 sec. The value for MI_(perm)(t) may then be computed using theoriginal phase, ψ_(ph)h(t), and the shifted amplitude, A_(amp)(t−Δt).MI(t) may then be deemed significant if it exceeding, for instance, 95%of the permuted values, MI_(perm)(t). A bootstrap calculation could alsobe performed to assess statistical significance or compute confidenceintervals.

In case of significant coupling, a phase of the frequency band f_(ph) atwhich the amplitude of the frequency band f_(amp) is greatest can bedetermined by finding the phase of the sinusoid that best fit themodulogram at each time point as follows:

$\begin{matrix}{{\Phi (t)} \equiv {{\arg \left\lbrack {\sum\limits_{n = 1}^{N}{e^{i\; \varphi_{n}}{M\left( {t,\varphi_{n}} \right)}}} \right\rbrack}.}} & (3)\end{matrix}$

In some aspects, an analysis of a spatial distribution ofphase-amplitude coupling may also be performed at process block 204using data obtained from multiple sensor locations about the subject'shead. In particular, a scalp topography of phase-amplitude coupling andEEG power may be performed, generating scalp patterns or maps. In someaspects, combined maps across multiple subjects may be produced, forexample, by taking a median of the population data.

To estimate the spatial distribution of cortical currents responsiblefor peak- and trough-max EEG coupling, for instance, a sourcelocalization analysis of the EEG signal may be performed using theminimum norm estimate (MNE) followed by phase-amplitude couplinganalysis of the estimated cortical current sources. For instance,Freesurfer software may be used to reconstruct tissue surfaces for theforward model based on high-resolution structural images, for example,obtainable using a magnetic resonance imaging system. A three layerboundary element forward model may be constructed using MNE software.

Localization analysis may use a reduced-dimension source space of say1284 patches of uniform current density, each for example, 1.25 cm indiameter, although other values may be possible. Source current timeseries estimated by MNE may then be used to calculate the MI, asdescribed above. In some aspects, to test significance within eachpatch, phase and amplitude may be resampled from within each subject'strough- or peak-max epoch to construct a null distribution for the MI.In some aspects, these may be combined across a number of subjects usingsurface-based registration to obtain an average cortical surface map. Ap-value for each patch was may be obtained by fitting a γ distributionto the group average null distribution; validity of the γ distributionmay be confirmed using Pearson's χ² test (for instance, at 95%confidence level). The p-values may be used to control the falsediscovery rate.

Referring again to FIG. 3, at process block 306, states of consciousnessof the subject may then be identified using information obtained fromanalyses performed at process block 304. For instance, determinedinformation at process block 306 may include phase-amplitude frequencydistributions, polarity of a phase-coupling modulation, spatialdistribution of phase-amplitude modulation, and so on.

Then at process block 308 a report may be generated, for example, in theform a printed report or, preferably, a real-time display. The reportmay include raw or processed data, signature information, r indicationsrelated to current or future brain states or levels of consciousness.Displayed signature information or determined states may be in the formof waveforms, spectrograms, coherograms, modulograms, probabilitycurves, maps, indices and so forth.

Specifically now referring to FIG. 4, an example system 400 inaccordance with the present disclosure is illustrated, for use inmonitoring and/or controlling a state of a subject during a medicalprocedure, or as result of an injury, pathology or other condition. Insome aspects, the system 400 could be used to guide or control, asnon-limiting examples, medically-induced coma, anesthesia, or sedation.In other aspects, the system 400 could be used to guide or controlmedically-induced hypothermia, for instance during hypothermia treatmentafter cardiac arrest, or during cardiac surgery.

The system 400 includes a subject monitoring device 412 that may includemultiple physiological sensors, such as EEG sensors. However, it iscontemplated that the subject monitoring device 412 may incorporateother sensors including blood oxygenation sensors, temperature sensors,acoustic respiration monitoring sensors, galvanic skin response sensorsand so forth.

The subject monitoring device 412 is connected via a cable 414 tocommunicate with a monitoring system 416, which may be a portable systemor device, and provides input of physiological data acquired from asubject to the monitoring system 416. Alternatively, the cable 414 andsimilar connections can be replaced by wireless connections betweencomponents. The monitoring system 416 may be configured to receive rawsignals acquired by the sensors and assemble, and even display, the rawor processed signals or information derived therefrom.

As illustrated in FIG. 4, the monitoring system 416 may be furtherconnected to a dedicated analysis system 418. In some aspects, theanalysis system 418 may receive the data from the monitoring system 416,and perform a phase-amplitude coupling analysis to identify brain statesof the subject, such as levels of consciousness, and generate a report,for example, as a printed report or, preferably, a real-time display ofsignature information and identified states. In some aspects, thesubject monitoring device 412 may be in communication with a portableprocessing system 410, for instance, as described with reference to FIG.2, which may be configured for perform any number of processing steps,such as identifying and/or relaying information relating to brain statesof a subject. Although shown as separate systems in FIG. 4, it is alsocontemplated that components and/or functionalities monitoring system418 and analysis system 418 and system 400 may be combined orintegrated.

In some configurations, the system 400 may also include a treatmentsystem 420. The treatment system 420 may be coupled to the analysissystem 418 and monitoring system 416, such that the system 400 forms aclosed-loop monitoring and control system. Such a closed-loop monitoringand control system may capable of a wide range of operation, and mayinclude a user interface 422, or user input, to allow a user toconfigure the closed-loop monitoring and control system, receivefeedback from the closed-loop monitoring and control system, and, ifneeded reconfigure and/or override the closed-loop monitoring andcontrol system.

In some configurations, the treatment system 420 may include a drugdelivery system not only able to control the administration ofanesthetic compounds for the purpose of placing the subject in a stateof reduced consciousness influenced by the anesthetic compounds, such asgeneral anesthesia or sedation, but can also implement and reflectsystems and methods for bringing a subject to and from a state ofgreater or lesser consciousness. In other configurations the treatmentsystem 420 may include a hypothermia treatment system. Other treatmentsmay be administered or facilitated by the treatment system 420 as well.

The above-described systems and methods may be further understood by wayof example. This example is offered for illustrative purposes only, andis not intended to limit the scope of the present invention in any way.Indeed, various modifications of the invention in addition to thoseshown and described herein will become apparent to those skilled in theart from the foregoing description and the following examples and fallwithin the scope of the appended claims. For example, specific examplesof brain states, medical conditions, levels of anesthesia or sedationand so on, in association with specific drugs and medical procedures areprovided, although it will be appreciated that other drugs, doses,states, conditions and procedures, may be considered within the scope ofthe present invention. Furthermore, examples are given with respect tospecific indicators related to brain states, although it may beunderstood that other indicators and combinations thereof may also beconsidered within the scope of the present invention. Likewise, specificprocess parameters and methods are recited that may be altered or variedbased on variables such as signal amplitude, phase, frequency, durationand so forth.

EXAMPLE

Ten healthy volunteers (5 male) were induced and allowed recovery fromgeneral anesthesia using the intravenous anesthetic propofol (2,6di-isopropyl-phenol). Propofol concentration increased in steps from 0to 5 mcg/ml every 14 min, followed by gradual reduction (see FIG. 1a ).A 64 channel EEG data was acquired using a BrainVision MRI Plus system(BrainAmp MRPlus, Brain-Products) with sampling rate 5 kHz, resolutiondensity (see FIG. 1c ) was computed using a multitaper method (window0.5 microV least significant bit, and bandwidth 0.016-1000 Hz. Galvanicskin response and plethysmography (PowerLab; AD Instruments) was alsorecorded. Electrodes, amplifiers, and filter settings provided accurateand unbiased recording of the entire frequency range analyzed in thisstudy (0.1-50 Hz). A Bayesian method was used to estimate thetime-varying probability of response (see FIG. 1b ). The time of loss ofconsciousness (LOC), tLOC, and return of consciousness (ROC), tROC, weredefined as the first and last times at which the median responseprobability was less than 0.05.

EEG data were re-referenced using a Laplacian montage by subtracting themean of each channel's nearest neighbors. The Laplacian montage improvesspatial localization of focal sources but attenuates EEG components withlow spatial frequencies. Because these components tend to have lowtemporal frequencies, it was also found to attenuate low temporalfrequencies. It was verified that the reference choice had a minorimpact on measurement of phase-amplitude coupling by repeating theanalysis using the average of mastoid electrodes as a reference. Rawsignals, x_(lo) (t), were first smoothed using an anti-aliasing filterand down-sampled to 250 Hz. Ultra-low-frequency drift was removed bysubtracting a piecewise quadratic spline fit to the signal with knotsevery 15 s. Bad channels were rejected by visual inspection. Aconservative procedure was adopted to remove low-frequency, largeamplitude EEG artifacts caused by movements while subjects were awake.The low-frequency signal, x_(lo)(t) was extracted by applying aband-pass filter (0.2-6 Hz), down-sampling to 5 Hz, and then applying amedial filter (window size 30 s) to |x_(lo)(t)|. Artifacts were definedas any time points for which |x_(lo)(t)| was at least 10-fold greaterthan this local threshold. Data within ±5 s of any artifact on eachchannel was excluded. Power spectral density (see FIG. 1c ) was computedusing a multitaper method (2 sec window size and 3 tapers).

In addition, eight subjects (5 male) with epilepsy intractable tomedication were implanted with intracranial subduralelectrocorticography (“ECoG”) electrodes for clinical monitoring (1 cmspacing, AdTech). Electrode placement was determined by clinicalcriteria, and covered regions within temporal, frontal, and parietalcortices (7 of 8 patients in the left hemisphere). Recordings werecollected during surgery for electrode explanation. Anesthesia wasadministered as a bolus of propofol according to standard clinicalprotocol based on the anesthesiologist's clinical judgment. ECOoG datawas sampled at 500 Hz with a single reference placed facing the dura inthe posterior parietal region. After visual rejection of bad channels,the same analysis of EEG data was applied, as described above.Individual MR and CT images were used to localize electrodes withrespect to the cortical surface. These locations were mapped to anaverage reference brain and combined across subjects for display.

Results obtained indicate that the general anesthetic drug propofolevokes distinct unconscious brain states with opposite patterns ofphase-amplitude coupling and unique source distributions, as illustratedin FIG. 5. Specifically, during controlled administration of profol(FIG. 5A) power spectral analysis showed that β power (12-20 Hz)increases at subanesthetic concentrations before loss of consciousness(“LOC”), followed by a sustained increase in low frequency activity(“LFA”) (0.1-1 Hz), and a frontally organized α rhythm (8-14 Hz)throughout the period of unconsciousness (FIG. 5C). By analyzingcross-frequency phase-amplitude coupling, it was discovered that thefrontal α rhythm waxes and wanes at specific phases of the LFA duringunconsciousness (FIG. 1D; gray bars). This coupling is also visible inraw EEG traces (FIGS. 5E and 5E).

Two distinct patterns of phase-amplitude coupling were observed duringthe unconscious period. At the threshold propofol concentration for LOC,c_(LOC), α amplitude was largest during the surface-negative phase ofthe LFA (trough-max). After an increase in propofol concentration, thephase-amplitude relationship abruptly reversed, with maximum α amplitudeat the surface-positive phase of the LFA (peak-max). The transitionbetween trough- and peak-max coupling occurred within a few minutes,after which the peak-max pattern remained until the propofolconcentration was reduced. The change in phase-amplitude coupling didnot coincide with the major increase in low-frequency EEG power, whichoccurred at LOC (FIG. 5C). This highlights the need for cross-frequencyanalysis to detect the shift in network dynamics.

All of study subjects (n=10) showed significant trough-max couplingduring the transition to and/or recovery from un-consciousness, andeight showed significant peak-max coupling at the highest propofolconcentrations (5 microg/ml) (FIGS. 6A-C). Overall, coupling wassignificant at frontal locations during 67% of unconscious epochs; only11% were significant before propofol administration. The medianmodulation index (“MI”), a measure of coupling strength, doubled from6×10⁻³ bits at baseline to 12×10⁻³ bits after LOC (p<10−3, Mann-WhitneyU test).

The frequency dependence of phase-amplitude coupling was also examinedAlthough significant phase-amplitude coupling was observed in somesubjects while they were awake before LOC (FIG. 5A), overall couplingbefore propofol administration was not consistent across subjects forany of the frequencies tested (0.2-50 Hz; FIGS. 6D-E). After LOC,phase-amplitude coupling was concentrated within the LFA and α bands.Trough-max coupling linked LFA and S phase (0.4-4 Hz) with and α low βamplitude (8.3-17 Hz). During peak-max epochs, separate patterns ofcoupling were found between a amplitude and the LFA (0.1-1 Hz) and 8(1-4 Hz) bands. α amplitude being the highest at the peaks of the LFA,whereas coupling to the δ band peaked about −π/2, (FIG. 6D)

Profound unconsciousness during general anesthesia, coma, andhypothermia can evoke a burst suppression pattern in whichhigh-frequency activity alternates with isoelectric periods. Burstsuppression is a distinct state from sleep slow waves and slowoscillations during general anesthesia, which are more regular andalternate at a faster rate. In the present study, 4 of 10 subjectsentered a state of burst suppression at the highest propofolconcentration. These subjects exhibited peak-max coupling during bothburst suppression and non-burst suppression epochs (FIG. 7). Peak-maxcoupling is therefore not simply a consequence of burst suppression butin stead represents a network state, which can occur at lower anestheticdrug doses, and also during the active burst period during burstsuppression.

Peak- and trough-max coupling had distinct spatial profiles over thescalp and across cortical regions. Traditional frequency-band analysishas identified the occipital-to-frontal shift in low-frequency (<40 Hz)scalp EEG power as a hallmark of general anesthesia. During thetransition to unconsciousness, herein it was found that trough-maxcoupling of a amplitude with LFA phase was likewise concentrated atfrontal electrodes (FIG. 8). However, peak-max coupling dominatedactivity throughout frontal, temporal, and posterocentral regions. Eachsubject's anatomical MRI was used to reconstruct cranial tissue surfacesand perform biophysical model-based source localization using a minimumnorm estimation algorithm (FIG. 9). This procedure is biased towarddistributed cortical sources, so these results represent a conservativeestimate of localization. Significant trough-max coupling in anteriorcingulate and frontal cortices bilaterally was found, whereas peak-maxcoupling extended throughout much of the cortex and was strongest infrontal and temporal lobes.

To directly examine the cortical sources of phase-amplitude coupledactivity with high spatial resolution, intra-cranial ECoG data wasrecorded using subdural electrode grids in 8 epilepsy subjects duringinduction of general anesthesia with propofol (605 total electrodes).After LOC, significant coupling appeared in frontal, occipital, andtemporal cortices (FIG. 10). The sign of the referential ECoG variesdepending on the geometry of local sources, resulting in a bimodaldistribution of coupling phase concentrated at 0 and π (FIG. 10).

Data herein that propofol consistently evokes two distinct corticalstates with opposite phase-amplitude coupling. These EEG patterns appearin a stereotyped progression during the induction of and recovery fromunconsciousness. In contrast with phase-amplitude coupling observedduring waking and sleep states, the patterns observed herein criticallyinvolve the frontal a rhythm, which is a hallmark of general anesthesia.Peak-max coupling resembles the well-studied slow oscillation, in whichscalp-positive waves are associated with transient activated corticalUP/ON states showing increased broadband EEG, local field potentialpower, and multiunit activity, whereas scalp-negative waves areassociated with quiescent DOWN/OFF states. It was found that peak-maxcoupling does not require a burst suppression activity pattern. Rather,peak-max coupling may be a general signature of unconsciousness in thecortex that precedes the onset of burst suppression.

The trough-max pattern parallels reports of increased corticalexcitability and attention during the surface-negative phase of slowcortical potentials. This pattern is localized in anterior cingulate andfrontal cortices, the most rostral portion of an ascending arousalsystem, including brainstem, thalamic, and cortical networks thatbecomes more metabolically active during emergence from propofol-inducedunconsciousness. Activity in anterior cingulate and frontal corticesalso covaries with spontaneous fluctuations in internal awareness; thetrough-max pattern might therefore be associated with propofol-inducedchanges in circuits mediating internal awareness.

The transition identified herein in cortical dynamics shows thatunconsciousness during propofol general anesthesia is not a unitarybrain state. Instead, propofol evokes at least two distinct states withopposite patterns of cross-frequency phase-amplitude coupling thatengage different cortical networks. The fact that these opposingmodulation patterns are mutually exclusive, with distinct spatialdistributions, suggests that alternation between widespread cortical ONand OFF states during peak-max coupling begins only after disruption ofthe frontal trough-max pattern. These objective EEG landmarks enablereliable interpretation of the physiology and functional significance ofcortical activity during unconsciousness.

FIGS. 5, 6A, and 6B show that the state of phase-amplitude modulationreflects varying levels of unconsciousness with increasing or decreasinganesthetic drug dose. These figures show that with increasing druglevels, and decreasing probability of response, the phase amplitudemodulation state changes from trough-max to peak-max, passing through anintermediate state where neither trough- nor peak-max modulation arepresent. FIGS. 8 and 9 show that the trough- and peak-max modulationpatterns reflect activity within distinct neural circuits, and thusreflect distinct states of unconsciousness with different propertiesthat may be clinically desired or clinically disadvantageous, dependingon the context. This information could be used to monitor and managesedation and general anesthesia by clinicians as well as by automatedcontrol algorithms. For instance, if an anesthesiologist observed thatthe patient transitioned into trough-max modulation from a deeper state(peak-max or the intermediate state between trough- and peak-max), itwould indicate that the patient was recovering consciousness. Thetrough-max state is localized to anterior cingulate and frontalcortices, which imply that a degree of internal consciousness may bepresent in patients displaying this pattern of modulation. Such patientsin a trough-max state could be aroused to responsiveness by externalstimuli. Consequently, during surgery under general anesthesia, thetrough-max state might reflect an inadequate state of impairedconsciousness for the clinical situation. In contrast, for surgical ormedical procedures requiring only sedation or monitored anesthesia care,where it might be desirable to have the patient respond to verbal ortactile stimuli and regain consciousness quickly, the trough-max statecould reflect a clinically-appropriate state of impaired consciousness.

The peak-max state, as well as the intermediate state between trough-andpeak-max, reflects a deeper level of impaired consciousness, in which itmay not be possible to be aroused to responsiveness by external stimuli.Such brain states might therefore be highly appropriate clinically forsurgery under general anesthesia where patients must remain unconsciousor unaware. Because the peak-max state precedes burst suppression, itcould be used as an indicator to prevent over-administration ofanesthetics into a burst suppression state.

A similar rationale could be employed to administer sedation to patientsin the intensive care unit. The trough-max modulation signal could beused to establish a state of sedation where patients are unconscious butcan be aroused to consciousness with verbal or tactile stimuli. Thestate in-between trough- and peak-max, as well as peak-max itself, couldbe an indicator of either deep sedation, or sedation that is too deep iflighter sedation is clinically indicated.

Clinical criteria as described above could be implemented in automatedcontrol algorithms to maintain brain states indicated by phase-amplitudemodulation during general anesthesia or sedation.

The frequency distribution for phase-amplitude modulation, asrepresented in FIG. 6D, for instance, could be used to help identifydifferent drugs used in anesthesia or other applications. For instance,different drugs may show different frequencies for phase, or differentfrequencies for amplitude, over which phase-amplitude modulation ispresent. Moreover, the specific phase relationship, indicated by themodulation phase in FIG. 6D, may differ according to drug. In anesthesiaand intensive care applications where multiple drugs are being used,this ability to distinguish between different drugs could be used tohelp identify which drug in a given combination is working in a dominantfashion. Such a determination could then be used to help adjust thecombination of drugs begin administered, or guide future administrationof drugs given the kinetics of the drug presently being administered.

The various configurations presented above are merely examples and arein no way meant to limit the scope of this disclosure. Variations of theconfigurations described herein will be apparent to persons of ordinaryskill in the art, such variations being within the intended scope of thepresent application. In particular, features from one or more of theabove-described configurations may be selected to create alternativeconfigurations comprised of a sub-combination of features that may notbe explicitly described above. In addition, features from one or more ofthe above-described configurations may be selected and combined tocreate alternative configurations comprised of a combination of featureswhich may not be explicitly described above. Features suitable for suchcombinations and sub-combinations would be readily apparent to personsskilled in the art upon review of the present application as a whole.The subject matter described herein and in the recited claims intends tocover and embrace all suitable changes in technology.

1. A system for monitoring consciousness of a subject experiencinganesthesia, the system comprising: a plurality of sensors placed aboutthe subject and configured to acquire electroencephalogram (“EEG”) datatherefrom while the subject is receiving anesthesia; at least oneprocessor configured to: receive the EEG data from the plurality ofsensors; perform a phase-amplitude coupling analysis using the receivedEEG data to determine a phase-amplitude frequency distribution; identifya state of consciousness of the subject using the determinedphase-amplitude frequency distribution; and generate a report indicativeof the state of consciousness of the subject.
 2. The system of claim 1,wherein the processor is further configured to determine a baselinephase-amplitude frequency distribution.
 3. The system of claim 2,wherein identifying the state of consciousness of the subject furthercomprises comparing the phase-amplitude frequency distribution to thebaseline phase-amplitude frequency distribution.
 4. The system of claim1 wherein the processor is further configured to determine a polarity ofa phase-coupling modulation associated with the received EEG data. 5.The system of claim 4, wherein identifying the state of consciousness ofthe subject further comprises comparing the polarity of thephase-coupling modulation to a baseline polarity of the phase-amplitudemodulation.
 6. The system of claim 1, wherein the processor is furtherconfigured to determine a spatial distribution of phase-amplitudecoupling using the received EEG data.
 7. The system of claim 1, whereinidentifying the state of consciousness of the subject further comprisescorrelating a dose of anesthesia with the determined phase-amplitudefrequency distribution.
 8. The system of claim 1, wherein identifyingthe state of consciousness of the subject further comprises determiningtrough-max and peak-max modulation patterns.
 9. The system of claim 8,wherein the trough- and peak-max modulation patterns are correlated withdistinct neural circuits to reflect distinct states of unconsciousness.10. The system of claim 9, wherein a transition into a trough-maxmodulation from a state of peak-max or intermediate state betweentrough- and peak-max indicates that the subject is recoveringconsciousness.
 11. The system of claim 10, wherein the trough-max statelocalized to anterior cingulate and frontal cortices indicates that adegree of internal consciousness may be present in the subject.
 12. Thesystem of claim 9, wherein a transition to a peak-max state indicates adeeper level of impaired consciousness.
 13. The system of claim 12,wherein the peak-max state is identified as a state that precedes burstsuppression.
 14. The system of claim 8, wherein the trough-max andpeak-max modulation patterns are related to the likelihood that thesubject could regain consciousness when presented external stimuli.